SARS-CoV-2-reactive IFN-γ-producing CD4+ and CD8+ T cells in blood do not correlate with clinical severity in unvaccinated critically ill COVID-19 patients

We examined the relationship between peripheral blood levels of SARS-CoV-2 S (Spike protein)1/M (Membrane protein)-reactive IFN-γ-producing CD4+ and CD8+ T cells, serum levels of biomarkers of clinical severity, and mortality in critically ill COVID-19 patients. The potential association between SARS-CoV-2-S-Receptor Binding Domain (RBD)-specific IgG levels in sera and mortality was also investigated. SARS-CoV-2 T cells and anti-RBD IgG levels were monitored in 71 non-consecutive patients (49 male and 22 female; median age, 65 years) by whole-blood flow cytometry and Enzyme-linked immunosorbent assay (ELISA), respectively (326 specimens). SARS-CoV-2 RNA loads in paired tracheal aspirates [TA] (n = 147) were available from 54 patients. Serum levels of interleukin-6, ferritin, D-Dimer, lactose dehydrogenase and C-reactive protein in paired sera were known. SARS-CoV-2 T cells (either CD4+, CD8+ or both) were detectable in 70 patients. SARS-CoV-2 IFN-γ CD4+ T-cell responses were documented more frequently than their CD8+ counterparts (62 vs. 56 patients) and were of greater magnitude overall. Detectable SARS-CoV-2 S1/M-reactive CD8+ and CD4+ T-cell responses were associated with higher SARS-CoV-2 RNA loads in TA. SARS-CoV-2 RNA load in TA decreased over time, irrespective of the dynamics of SARS-CoV-2-reactive CD8+ and CD4+ T cells. No correlation was found between SARS-CoV-2 IFN-γ T-cell counts, anti-RBD IgG concentrations and biomarker serum levels (Rho ≤ 0.3). The kinetics of both T cell subsets was comparable between those who died or survived, whereas anti-RBD IgG levels were higher across different time points in deceased patients than in survivors. Enumeration of peripheral blood levels of SARS-CoV-2-S1/M-reactive IFN-γ CD4+ and CD8+ T cells does not predict viral clearance from the lower respiratory tract or poor clinical outcomes in critically ill COVID-19 patients. In contrast, anti-RBD IgG levels were directly associated with increased mortality.


Patients and methods
Patients and specimens. This observational, prospective and longitudinal study included a total of 71 non-consecutive critically ill patients, (49 male and 22 female; median age, 65 years; range, 21-80 years, as previously defined 27 ) with COVID-19 microbiologically documented by RT-PCR in nasopharyngeal specimens collected prior to recruitment at the intensive care unit (ICU) between October 2020 and February 2021. These patients were included in previous studies [28][29][30] assessing the rate and kinetics of SARS-CoV-2 RNAemia and Nucleocapsid (N) antigenemia, as well as the potential role of SARS-CoV-2-Spike-targeting antibodies in mediating SARS-CoV-2 RNAemia and N-antigenemia clearance. No data on SARS-CoV-2 T-cell responses were included in these studies. The only patient inclusion criterion was availability of whole blood specimens for analyses, which were scheduled for once-weekly collection during ICU stay. No patient had received COVID-19 vaccination at ICU admission. Medical history and laboratory data were retrospectively reviewed. The most relevant patient characteristics are shown in Table 1. The current study was approved by the Research Ethics Committee of Hospital Clínico Universitario INCLIVA (May 2020). Informed consent was obtained from participants either on the hospital ward or at the time of ICU admission. www.nature.com/scientificreports/ SARS-CoV-2 RNA load in tracheal aspirates. Undiluted tracheal aspirates (TA) were obtained through a Halyard Turbo-cleaning closed suction system, which was connected to an orotracheal tube as a standard of care during the pandemic. SARS-CoV-2 RNA quantitation in TA was carried out by the Abbott RealTime SARS-CoV-2 assay Abbott Molecular (Des Plaines, IL, USA) 29,30 . SARS-CoV-2 viral loads (in copies/mL) were estimated using the AmpliRun Total SARS-CoV-2 RNA Control (Vircell SA, Granada, Spain).

SARS-CoV-2 RBD IgG immunoassay.
Serum levels of SARS-CoV-2 RBD IgG were measured as previously described 33 . Briefly, SARS-CoV-2 RBD was produced in Sf9 insect cells infected with recombinant baculoviruses (Invitrogen, CA, USA). Following purification, the protein was concentrated to 5 mg/mL by ultrafiltration. Ninety-six well microplates were coated with RBD at 1 μg/mL. Serum samples were diluted 1:500 in phosphate-buffered saline-Tween (PBS-T) containing 1% bovine serum albumin and run in triplicate (mean values are reported). The plates were incubated with 1:5000 dilution of horseradish peroxidase (HRP)-conjugated goat anti-human IgG (Jackson Laboratories). After three washes with PBS-T, the binding was detected using SigmaFast OPD reagent (Sigma) according to the manufacturer's recommendations. Color development was stopped with 3 M H 2 SO 4 and read on a Multiskan FC (ThermoFisher Scientific) plate reader at 492 nm. Serial sera from individual patients were analyzed in the same run. The cut-off discriminating between positive and negative sera was set as the mean absorbance of control sera plus three times the standard deviation.

Statistical methods.
Frequency comparisons for categorical variables were carried out using the Fisher exact test. Differences between medians were compared using the Mann-Whitney U test. Spearman's rank test was used for analysis of correlation between continuous variables. Two-sided exact P-values were reported. A P-value < 0.05 was considered statistically significant. The analyses were performed using SPSS version 20.0 (SPSS, Chicago, IL, USA).
Ethical statement. The

Dynamics of SARS-CoV-2-reactive IFN-γ CD4 + and CD8 + T cells in intensive care COVID-19 patients.
A total of 326 whole blood specimens were available for assessment of SARS-CoV-2 S1/M-reactive  Table 2). SARS-CoV-2 IFN-γ CD4 + T-cell levels appeared to fluctuate over the first 5 weeks after symptom onset, and increase at later times; in contrast, SARS-CoV-2 IFN-γ CD8 + T cells waned over time. Importantly, neither the use of remdesivir nor that of tocilizumab had an impact on median levels of SARS-CoV-2 CD4 + and CD8 + T cells (not shown). Also relevant, SARS-CoV-2 T cells could be detected, even at high frequencies, in patients undergoing corticosteroids therapy (see Supplementary Fig. 1 for a representative example). Moreover, patient age was not correlated with SARS-CoV-2 IFN-γ CD8 + (Rho = 0.2; P = 0.25) or CD4 + (Rho = 0.1; P = 0.1) T-cell counts. Next, we examined the kinetics of SARS-CoV-2 T-cell responses at the individual level in 47 patients with ≥ 3 available specimens (Supplementary Table 3). Qualitatively, many patients exhibited fluctuating CD8 + and CD4 + T-cell responses (n = 25 for both T-cell subsets), while fewer tested positive (4 for CD8 + and 8 for CD4 + ) or negative (10 for CD8 + and 3 for CD4 + ) systematically over time.

SARS-CoV-2-reactive IFN-γ CD4 + and CD8 + T cells and SARS-CoV-2 RNA load in the lower respiratory tract.
We next investigated the potential impact of SARS-CoV-2 RNA load in tracheal aspirates on the detection rate and magnitude of SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses. A total of 147 paired TA and whole blood specimens from 54 patients were available for analyses; these specimens were collected at a median of 21 days (range, 2-71 days) since symptom onset. As shown in Fig. 2, higher SARS-CoV-2 RNA loads in TA were associated with measurable SARS-CoV-2 S1/M-reactive CD8 + ( Fig. 2A) and CD4 + (Fig. 2B) T-cell responses (P = 0.01 and P = 0.06, respectively) in paired whole-blood specimens. Additionally, a trend towards higher SARS-CoV-2 S1/M-reactive IFN-γ CD8 + and CD4 + T-cell counts (P = 0.13 and P = 0.15) was documented when SARS-COV-2 RNA (at any level) could be detected in paired TA specimens (Fig. 2C,D, respectively). However, SARS-CoV-2 CD8 + and CD4 + T cells correlated either poorly (Rho = 0.20) or not at all (Rho = 0.09) with SARS-CoV-2 RNA loads ( Supplementary Fig. 2). Removal of specimens with undetectable (negative) results from correlation analyses had no impact on the results (not shown). Following this, we examined the dynamics of SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses in peripheral blood relative to that of SARS-COV-2 RNA load in the lower respiratory tract. This analysis involved 14 patients (n = 53 specimens) with ≥ 3 whole blood and TA paired specimens. As shown in Table 2, no obvious (inverse) relationship between the dynamics of SARS-CoV-2 RNA load in TA and peripheral levels of SARS-CoV-2-reactive CD8 + and CD4 + T cells was noticed; in fact, while SARS-CoV-2 RNA load clearly decreased over time, SARS-CoV-2 CD8 + T cells fluctuated within the first 4 weeks since symptom onset and tended to wane subsequently, while SARS-CoV-2 CD4 + T-cell counts fluctuated within the first 5 weeks since symptom onset and slightly increased afterwards.

SARS-CoV-2-reactive IFN-γ CD4 + and CD8 + T cells and serum levels of clinical severity biomarkers.
Since development of adaptive immunity responses may be modulated in magnitude and breadth by the net state of inflammation 34 , we next investigated whether serum levels of IL-6, ferritin D-Dimer, LDH and CRP correlated with SARS-CoV-2-reactive IFN-γ CD4 + and CD8 + T-cell levels in paired whole blood specimens. Median levels of all these biomarkers were comparable across patients either with or without detectable T-cell responses at the corresponding sample time (Table 3). Furthermore, no correlation was found between whole blood T-cell counts and biomarker serum levels (Table 4).  Table 2. Kinetics of SARS-CoV-2 RNA load in tracheal aspirates and SARS-CoV-2 S1/M-reactive IFN-γ CD8 + and CD4 + T cells in paired whole blood specimens from critically ill patients.

Discussion
While impaired SARS-CoV-2-reactive functional T-cell responses have been linked to progression from mild to severe forms of COVID-19 4,5,7,8,10-15 , it remains to be elucidated whether these are associated with clinical outcomes among critically ill patients. Here, we prospectively monitored SARS-CoV-2 S1/M-reactive IFN-γ Table 3. SARS-CoV-2 S1/M-reactive IFN-γ CD8 + and CD4 + T-cell counts in whole blood and serum levels of clinical severity biomarkers in critically ill patients.

Median cell counts in cells/μL (range)
IL-6 (pg/mL)  Table 4. Correlation between SARS-CoV-2 S1/M-reactive IFN-γ CD8 + T-cell counts in whole blood and serum levels of biomarkers of clinical severity in critically ill patients. a Spearman rank test. www.nature.com/scientificreports/ T-cell responses using an in-house-developed flow cytometry assay in a cohort of 71 patients admitted to ICU, of whom most were mechanically ventilated (88%) and 28 died. Our antigen choice was based upon previously published data showing that a wide array of highly immunogenic T-cell epitopes map within S1 and M proteins that elicit immunodominant responses 2,4-8 . In addition to further characterizing the dynamics of these T-cell subsets in this population group, which currently remains poorly defined, we aimed to establish whether SARS-CoV-2 S1/M-reactive IFN-γ T-cell counts were related to serum levels of biomarkers which predict poor outcomes and mortality across critically ill patients, and could thus be used as a surrogate prognostic marker. Our data allowed us to draw the following conclusions. First, virtually all patients, irrespective of age, developed SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses (either CD8 + , CD4 + or both) during ICU stay, although CD4 + T-cell responses were detected more frequently and at higher levels than their CD8 + counterparts. Furthermore, overall, CD4 + T-cell responses appeared to fluctuate over time, while those involving CD8 + T cells tended to wane. Despite this general landscape, we noted wide variations at the individual level. In fact, fluctuating responses were observed more frequently than consistent (either detectable or undetectable) ones over time. Moreover, transition from detectable to undetectable T-cell responses (or vice versa) was not uncommon, warranting further studies to confirm this latter finding and clarify the underlying pathogenetic mechanism. Second, data obtained in the rhesus macaque experimental model clearly underscore the crucial role of SARS-CoV-2-reactive T-cell responses in contributing to virus clearance from the lower respiratory tract 35,36 . To determine whether this could be the case in our patient population, we compared the dynamics of SARS-CoV-2 load in TA to that of SARS-CoV-2 S1/M-reactive IFN-γ T cells in paired whole-blood specimens. Our data indicated that although the rate of detection and magnitude of SARS-CoV-2 T-cell responses appeared directly related to the level of virus replication in the lower respiratory tract, as inferred by viral RNA load in TA, the dynamics of virus clearance from this compartment was not consistently associated with that of peripheral blood SARS-CoV-2  www.nature.com/scientificreports/ S1/M-reactive IFN-γ T cells. This suggested that enumeration of these T-cell subset specificities in whole blood provides no reliable information on the course of virus infection in the lungs. Naturally, our findings do not detract from the role of T cells in affording protection against severe forms of COVID-19, but rather suggest that examination of SARS-CoV-2-driven immune responses at the lower respiratory tract could offer a better perspective of the interplay between virus replication and host immune responses during severe COVID-19. Indeed, different cellular immune profiles in the airways and blood have been documented in critically ill COVID-19 patients 37,38 . Moreover, activated tissue-resident T cell frequencies were correlated with survival 39 and aberrant T cell responses were detected in bronchoalveolar lavages from most severe COVID-19 patients 40 . Third, sustained high serum levels of several biomarkers of inflammation (IL-6, ferritin, CRP), coagulation and fibrinolysis (D-dimer) and tissue damage (LDH) are associated with poor COVID-19 prognosis across critically ill patients 41,42 . Hyperinflammatory states may also down-regulate ongoing T-cell responses 34 . In this context, we investigated whether (qualitative and quantitative) SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses in our patients were somehow related to levels of the aforementioned biomarkers. This was found not to be the case, as serum levels of all biomarkers were similar regardless of detected or absent SARS-CoV-2 CD8 + or CD4 + T-cell responses; moreover, no correlation was found between SARS-CoV-2 T-cell counts and biomarker levels in paired specimens. Fourth, in our cohort, mortality was not consistently associated with either detection rate or the magnitude of SARS-CoV-2 S1/M-reactive IFN-γ T-cell responses. This is in line with data reported by Thieme and colleagues 16 , who found that development of robust T cell responses toward spike, membrane, and nucleocapsid SARS-CoV-2 proteins was not associated with survival in a small cohort of critical COVID-19 patients. In a more comprehensive study, Saris et al. 37 found high levels of TNF-α-producing S-reactive CD8 + T cells to be associated with increased mortality, while mono-functional CD4 + T-cell subsets could not be related to survival; nevertheless, survivors appeared to display broader and stronger virus-reactive poly-functional CD4 + T-cell responses than those who died; yet, as stated by the authors, no obvious combination of effector functions of CD4 + T cells could be linked to prognosis. The key finding of the study 37 was that mucosal-associated invariant T (MAIT) cell activation is an independent and significant predictor of mortality. Likewise, in a very small study critically ill patients with hypertension who died exhibited prolonged low peripheral blood counts of SARS-CoV-2-S-reactive CD8 + and CD4 + T cells 20 .

SARS-CoV-2 S1/M-reactive IFN-γ CD8 + T cells
A number of previous studies have suggested that antibodies targeting the SARS-CoV-2 S protein are present at higher levels in patients presenting with severe forms of COVID-19, compared to patients exhibiting milder clinical forms [21][22][23][24] . However, information regarding the dynamics of SARS-CoV-2-S antibodies in ICU patients is scarce. Overall, we found anti-RBD IgG levels to increase during ICU stay within the first 5 weeks after symptom onset; interestingly, this increase was greater in patients who died than in those who survived. The limited number of death events in our series precludes further statistical analyses assessing whether anti-RBD IgG level behaves as an independent risk factor for mortality. In a previous study, Martín-Vicente et al. 26 reported that low anti-SARS-CoV-2 S antibody levels at ICU admission predict mortality in critical COVID-19 patients. Our data do not support this idea. Differences in the characteristics of patients and timing of specimen collection since symptom onset may account, at least partly, for this discrepancy. Whether anti-RBD IgG may heighten the risk of death in ICU patients warrants further clinical and pathogenetic research. In this context, we found either weak or no correlation between anti-RBD and biomarkers of clinical severity, in line with a previous report by our group 33 , thus suggesting that anti-RBD IgG are unlikely to be involved in promoting inflammation and vascular damage in ICU patients. Moreover, the lack of correlation between anti-RBD IgG levels and SARS-CoV-2 RNA levels in TA argue against a major role of this antibody specificity in mediating virus clearance from the lower respiratory tract.
The current study has several limitations deserving of comment. First, the limited sample size, particularly regarding the number of deceased patients, clearly undermines the robustness of the analyses. Sufficiently powered studies are needed to clarify whether monitoring SARS-CoV-2 T-cell responses in peripheral blood may have prognostic value in critically ill COVID-19 patients. Second, although blood specimens were scheduled to be collected weekly, this was unfortunately not achieved in a number of patients. Third, like other flow cytometrybased immunoassays used for measuring SARS-CoV-2 T-cell responses, ours lacks appropriate standardization, although it is worth noting that our flow cytometry assay was found to be more sensitive than the commerciallyavailable QuantiFERON SARS-CoV-2 (an interferon-gamma release assay) for detection of SARS-CoV-2 T cells in blood 43 . Fourth, SARS-CoV-2-reactive T cells were examined only for IFN-γ production, thus we cannot rule out the possibility that other functional T-cell specificities are associated with survival. Also, no data on the state of differentiation of reactive T cells are provided. Fifth, only SARS-CoV-2 S1 and M-reactive T cells were measured; whether enumeration of SARS-CoV-2 T cells targeting other viral proteins may help to individualize mortality risk in critical COVID-19 patients remain to be defined. Sixth, SARS-CoV-2 T-cell responses in the lower respiratory compartment were not assessed. Seventh, SARS-CoV-2 neutralizing antibodies were not measured. Eighth, most patients were under corticosteroid treatment within sampling times. Ninth, the impact of tocilizumab use on serum levels of inflammatory biomarkers was not apparent in our series (not shown), although it cannot be completely dismissed. Finally, a variable number of specimens were missing at the different timeframes explored; this may have skewed the T cell responses reported.
In summary, we found no association of peripheral blood levels of SARS-CoV-2-S1/M-reactive IFN-γ CD4 + and CD8 + T cells or anti-RBD IgG levels with viral clearance from the lower respiratory tract or serum levels of biomarkers of poor prognosis in ICU patients. Interestingly, while SARS-CoV-2-S1/M-reactive IFN-γ CD4 + and CD8 + T-cell dynamics were seemingly not associated with mortality, increased levels of anti-RBD IgGs were observed in patients who died compared to survivors. Further, larger studies centered on resolving these issues should be conducted.